AI-driven Technologies for Informed Decisions in Healthcare

AI-driven Technologies for Informed Decisions in Healthcare

Healthcare Tech Outlook | Thursday, January 17, 2019

Artificial intelligence (AI) with machine learning and deep learning has disrupted the healthcare sector to improve customer relations, operations, and marketing and research methods. They provide computers to process a plethora of structured and unstructured data and determine patterns and predictions, contributing to more informed decisions in the future. Machine learning, however, requires human assistance in entering parameters that computers need to recognize, but these inputs can take time. The next step is deep learning. Machine learning and deep learning are the basics of AI that contribute to a holistic approach to the data generated in healthcare. For patient care, AI has a massive impact. Earlier, diagnosis and improved risk identification resulted in faster processing speeds and models that were trained to identify things that could escape the eye. It has also succeeded in reducing the time period of cancer treatment, accelerate genetic analysis, and develop customer treatment more quickly and reduce costs.

In addition to improving the accuracy of the diagnosis of medical imaging, AI makes it easier to personalize treatment planning and deliver results at the earliest. This not only tends to increase the productivity of the radiologists, and also achieves profitability earlier than expected. Although AI was once seen as a threat in the medical imaging community, it is no longer seen with that perspective today. The demand for AI in the radiologist community has increased significantly, and investment in AI imaging technology is rising.

There could be problems in regard to patient-physician communication or communication between different healthcare bodies. The serious medical errors occur during patient transfer due to miscommunication between caregivers. AI can tackle these communication problems more effectively. Apple has already introduced the ResearchKit and CareKit model that allows researchers and developers to create medical apps to interact with patients and monitor them.

According to Accenture's research, the effective application of AI to mitigate medical dosage inaccuracies can save about $16 billion for the healthcare industry by 2026. AI is widely seen as an effort to process and interpret massive amounts of data, and it will be a major advantage in 2019 and beyond making it possible to apply and administer medications with the precise dosage. The communication problem of the healthcare sector can be tackled easily and quickly by AI and can expect to see serious developments in 2019. Medical experts predict that AI will have an important impact throughout the continuum of healthcare in areas like patient engagement, chronic disease management, clinical decision-making, and financial modeling.

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